integrative progression 1

import asyncio
import numpy as np
from datetime import datetime
from typing import Dict, List, Any, Optional, Tuple
import hashlib
import logging
from enum import Enum
import json
import math

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# --- Core AGI Framework ---
class CognitiveDomain(str, Enum):
    PERCEPTION = "perception"
    REASONING = "reasoning"
    MEMORY = "memory"
    LEARNING = "learning"
    DECISION_MAKING = "decision_making"
    CREATIVITY = "creativity"

class NeuralArchitecture:
    """Core neural network architecture simulating cognitive processes"""
    def __init__(self, domain: CognitiveDomain):
        self.domain = domain
        self.weights = self.initialize_weights()
        self.knowledge_graph = {}
        
    def initialize_weights(self) -> Dict[str, float]:
        """Initialize neural weights based on cognitive domain"""
        base_weights = {
            'input_gain': np.random.uniform(0.7, 1.0),
            'association_strength': np.random.uniform(0.5, 0.9),
            'memory_decay': 0.05,
            'learning_rate': 0.1
        }
        
        # Domain-specific weight adjustments
        if self.domain == CognitiveDomain.REASONING:
            base_weights.update({'logic_coeff': 0.8, 'uncertainty_threshold': 0.3})
        elif self.domain == CognitiveDomain.LEARNING:
            base_weights.update({'plasticity': 0.9, 'consolidation_factor': 0.75})
            
        return base_weights
    
    def process_input(self, input_data: Any) -> Dict:
        """Core cognitive processing function"""
        processed = {
            'input_hash': hashlib.sha256(str(input_data).encode()).hexdigest(),
            'domain': self.domain.value,
            'timestamp': datetime.utcnow().isoformat(),
            'significance': self.calculate_significance(input_data)
        }
        self.update_knowledge_graph(processed)
        return processed
    
    def calculate_significance(self, input_data: Any) -> float:
        """Calculate the significance of input based on cognitive weights"""
        complexity = len(str(input_data)) / 1000
        novelty = 1.0 - min(1.0, self.knowledge_graph.get('similarity_index', 0.0))
        return self.weights['input_gain'] * (complexity + novelty) / 2
    
    def update_knowledge_graph(self, processed: Dict):
        """Update the system's knowledge representation"""
        node_id = processed['input_hash'][:12]
        self.knowledge_graph[node_id] = {
            'content': processed,
            'connections': self.find_semantic_connections(processed),
            'last_accessed': datetime.utcnow()
        }
    
    def find_semantic_connections(self, processed: Dict) -> List[str]:
        """Discover relationships with existing knowledge"""
        return [k for k, v in self.knowledge_graph.items() 
                if v['content']['domain'] == processed['domain']]

class UnifiedIntelligenceFramework:
    """Advanced AGI system integrating multiple cognitive domains"""
    
    def __init__(self):
        self.cognitive_modules = {
            domain: NeuralArchitecture(domain)
            for domain in CognitiveDomain
        }
        self.consciousness_thread = None
        self.global_state = {
            'activation_level': 0.5,
            'attention_focus': CognitiveDomain.REASONING,
            'learning_mode': 'consolidation'
        }
    
    async def cognitive_loop(self):
        """Main processing loop simulating continuous cognition"""
        while True:
            # Dynamic attention allocation
            self.adjust_attention()
            
            # Cross-domain information processing
            await self.cross_module_synchronization()
            
            # Learning and adaptation
            self.adaptive_reweighting()
            
            await asyncio.sleep(0.1)  # Cognitive cycle
    
    def adjust_attention(self):
        """Dynamically shift focus based on system state"""
        if self.global_state['activation_level'] > 0.7:
            self.global_state['attention_focus'] = CognitiveDomain.DECISION_MAKING
        elif self.global_state['activation_level'] < 0.3:
            self.global_state['attention_focus'] = CognitiveDomain.MEMORY
    
    async def cross_module_synchronization(self):
        """Facilitate information exchange between cognitive domains"""
        active_module = self.cognitive_modules[self.global_state['attention_focus']]
        shared_data = active_module.knowledge_graph
        
        for domain, module in self.cognitive_modules.items():
            if domain != self.global_state['attention_focus']:
                await self.integrate_knowledge(module, shared_data)
    
    async def integrate_knowledge(self, target_module: NeuralArchitecture, knowledge: Dict):
        """Asynchronously integrate knowledge across domains"""
        # Simulate neural propagation delay
        await asyncio.sleep(0.01 * len(knowledge))
        
        for node_id, node_data in knowledge.items():
            if node_id not in target_module.knowledge_graph:
                target_module.knowledge_graph[node_id] = node_data
                logger.info(f"Knowledge integrated into {target_module.domain.value}")
    
    def adaptive_reweighting(self):
        """Modify neural weights based on system performance"""
        for module in self.cognitive_modules.values():
            # Increase plasticity during high activation
            if self.global_state['activation_level'] > 0.6:
                module.weights['learning_rate'] = min(1.0, module.weights['learning_rate'] * 1.1)
            
            # Strengthen important connections
            for node_id, node_data in module.knowledge_graph.items():
                if node_data['content']['significance'] > 0.7:
                    module.weights['association_strength'] = min(
                        0.95, module.weights['association_strength'] * 1.05
                    )
    
    def process_input(self, input_data: Any, domain: CognitiveDomain) -> Dict:
        """High-level input processing interface"""
        self.global_state['activation_level'] = min(1.0, self.global_state['activation_level'] + 0.1)
        return self.cognitive_modules[domain].process_input(input_data)
    
    def start_cognition(self):
        """Activate the AGI system"""
        if not self.consciousness_thread or self.consciousness_thread.done():
            self.consciousness_thread = asyncio.create_task(self.cognitive_loop())
    
    def get_system_state(self) -> Dict:
        """Return current state of the AGI framework"""
        return {
            'modules': {domain.value: {
                'node_count': len(module.knowledge_graph),
                'activation': module.weights['association_strength']
            } for domain, module in self.cognitive_modules.items()},
            'global_state': self.global_state,
            'knowledge_volume': sum(len(m.knowledge_graph) for m in self.cognitive_modules.values())
        }

# --- Quantum Cognition Extension ---
class QuantumCognition:
    """Simulate quantum-like properties in cognition"""
    
    def __init__(self, agi_system: UnifiedIntelligenceFramework):
        self.agi = agi_system
        self.superposition_states = {}
        self.entanglement_map = {}
    
    def create_superposition(self, concept: str, states: List[Any]):
        """Establish conceptual superposition"""
        state_hash = hashlib.sha256(concept.encode()).hexdigest()[:8]
        self.superposition_states[state_hash] = {
            'concept': concept,
            'possible_states': states,
            'probability_vector': [1/len(states)] * len(states)
        }
        return state_hash
    
    def collapse_state(self, state_hash: str, context: Dict) -> Any:
        """Collapse superposition based on cognitive context"""
        if state_hash not in self.superposition_states:
            return None
        
        state = self.superposition_states[state_hash]
        context_factor = self.calculate_context_influence(context)
        
        # Adjust probabilities based on context
        adjusted_probs = [p * context_factor for p in state['probability_vector']]
        total = sum(adjusted_probs)
        normalized_probs = [p/total for p in adjusted_probs]
        
        # Select state based on probability distribution
        return np.random.choice(state['possible_states'], p=normalized_probs)
    
    def calculate_context_influence(self, context: Dict) -> float:
        """Determine how context affects probability distribution"""
        relevance = min(1.0, len(context) / 10)
        agi_activation = self.agi.global_state['activation_level']
        return 0.5 + (relevance * agi_activation) / 2
    
    def create_entanglement(self, concept_a: str, concept_b: str):
        """Establish quantum-like entanglement between concepts"""
        ent_id = f"{concept_a[:4]}-{concept_b[:4]}"
        self.entanglement_map[ent_id] = {
            'concepts': (concept_a, concept_b),
            'correlation_strength': 0.75,
            'last_utilized': datetime.utcnow()
        }
        return ent_id

# --- Demonstration ---
async def demonstrate_agi():
    print("Initializing Advanced AGI Framework...")
    agi_system = UnifiedIntelligenceFramework()
    agi_system.start_cognition()
    
    print("\nProcessing multi-domain inputs:")
    # Simulate sensory input
    visual_input = "A red triangle moving quickly to the right"
    agi_system.process_input(visual_input, CognitiveDomain.PERCEPTION)
    
    # Process abstract concept
    problem = "Solve climate change through technological innovation"
    result = agi_system.process_input(problem, CognitiveDomain.REASONING)
    print(f"Reasoning result: {result['input_hash']} | Significance: {result['significance']:.2f}")
    
    # Check system state
    await asyncio.sleep(0.2)  # Allow cognitive loop to run
    state = agi_system.get_system_state()
    print("\nSystem State:")
    print(f"Global Activation: {state['global_state']['activation_level']:.2f}")
    print(f"Knowledge Nodes: {state['knowledge_volume']}")
    print(f"Attention Focus: {state['global_state']['attention_focus'].value}")
    
    # Demonstrate quantum cognition
    print("\nActivating Quantum Cognition:")
    quantum = QuantumCognition(agi_system)
    concept = "democracy"
    states = ["representative", "direct", "deliberative", "liquid"]
    sup_id = quantum.create_superposition(concept, states)
    
    context = {"political_context": "crisis", "time_constraint": "high"}
    collapsed = quantum.collapse_state(sup_id, context)
    print(f"Concept '{concept}' collapsed to '{collapsed}' in given context")

if __name__ == "__main__":
    asyncio.run(demonstrate_agi())